Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance C. Hogrefe 1,* , P. Doraiswamy 2,** , B. Colle 3 , K. Demerjian 2 , W. Hao 4 , M. Erickson 3 , M. Souders 3 , and J.-Y. Ku 4 1 U.S. Environmental Protection Agency, Research Triangle Park, NC, USA, 2 Atmospheric Sciences Research Center, University at Albany, Albany, NY, USA, 3 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA, 4 New York State Department of Environmental Conservation, Albany, NY, USA, * Previously at 2,4 , ** Now at RTI International, Research Triangle Park,, NC, USA Abstract In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations con- sidered in this study introduce a typical variability of ~1°C, 250 - 500 m, 1 m/s, and 15 - 30° for temperature, PBL height, wind speed, and wind direction, respectively. The effects of grid resolution are typically smaller and more localized. Results of the air quality simu- lations show that the perturbations in meteorology tend to have a larger impact on pollutant concentrations than emission perturba- tions and grid resolution effects. Operational model evaluation re- sults show that the meteorological and grid resolution ensembles impact a wider range of model performance metrics than emission perturbations. Probabilistic model performance was found to vary with exceedance thresholds. The results of this study suggest that meteorological perturbations introduced through ensemble weather forecasts are the most important factor in constructing a model- based O 3 and PM 2.5 ensemble forecasting system. Keywords: Ensemble Modeling, Model Evaluation, Direct Decou- pled Method